基于Python爬虫的淘宝服装数据分析项目

文章目录

一.项目介绍
二.爬虫代码
- 代码分析
三. 数据处理
四. 数据可视化

一.项目介绍

该项目是基于Python爬虫的淘宝服装数据分析项目，以致于帮助商家了解当前服装市场的需求，制定更加精确的营销策略。首先，需要爬取淘宝中关于服装的大量数据，然后对数据进行处理和分析，比如处理重复值，提取关键信息，统计分析等等，最后，通过Echarts将处理后的数据进行可视化展示，可以更为直观的看到服装市场的趋势和变化。

二.爬虫代码

import json
import os
import requests
from selenium import webdriver
from selenium.common.exceptions import TimeoutException
from selenium.webdriver.common.by import By
from selenium.webdriver.support.ui import WebDriverWait
from selenium.webdriver.support import expected_conditions as EC
from pyquery import PyQuery as pq # 作用：解析网页
import time
import openpyxl as opclass TaobaoCrawler:# 初始化def __init__(self, keyword, start_page, end_page):self.keyword = keywordself.start_page = start_pageself.end_page = end_pageself.count = 1self.driver = self.init_driver()self.wait = WebDriverWait(self.driver, 10)self.wb, self.ws = self.init_excel()self.products = []self.create_images_folder()# 创建图片文件夹def create_images_folder(self):if not os.path.exists('images'):os.makedirs('images')# 下载图片def download_image(self, img_url, img_name):if img_url:response = requests.get(img_url)if response.status_code == 200:with open(f'images/{img_name}.png', 'wb') as f:f.write(response.content)else:print(f"Image URL is None for {img_name}, skipping download.")# 初始化浏览器def init_driver(self):options = webdriver.ChromeOptions()options.add_experimental_option("excludeSwitches", ['enable-automation'])driver = webdriver.Chrome(options=options)driver.maximize_window()return driver# 初始化Exceldef init_excel(self):wb = op.Workbook()ws = wb.create_sheet(index=0)title_list = ['Page', 'Num', 'Title', 'Price', 'Deal', 'Location', 'Shop', 'IsPostFree', 'Title_URL', 'Shop_URL', 'Img_URL']for i, title in enumerate(title_list):ws.cell(row=self.count, column=i + 1, value=title)self.count += 1return wb, ws# 搜索商品def search_goods(self):try:# 搜索商品print('正在搜索: ')self.driver.get('https://www.taobao.com')self.driver.execute_cdp_cmd("Page.addScriptToEvaluateOnNewDocument",{"source": """Object.defineProperty(navigator, 'webdriver', {get: () => undefined})"""})input = self.wait.until(EC.presence_of_element_located((By.CSS_SELECTOR, "#q")))# 提交搜索submit = self.wait.until(EC.element_to_be_clickable((By.CSS_SELECTOR, '#J_TSearchForm > div.search-button > button')))# 输入关键字input.send_keys(self.keyword)submit.click()# 等待页面加载完成if self.start_page != 1:time.sleep(3)page_input = self.wait.until(EC.presence_of_element_located((By.XPATH, '//*[@id="pageContent"]/div[1]/div[3]/div[4]/div/div/span[3]/input')))page_input.send_keys(self.start_page)admit = self.wait.until(EC.element_to_be_clickable((By.XPATH, '//*[@id="pageContent"]/div[1]/div[3]/div[4]/div/div/button[3]')))admit.click()# 获取商品信息self.get_goods(self.start_page)for i in range(self.start_page + 1, self.end_page + 1):self.page_turning(i)# 捕获异常except TimeoutException:print("search_goods: error")self.search_goods()# 翻页def page_turning(self, page_number):print('正在翻页: ', page_number)try:time.sleep(2)submit = self.wait.until(EC.element_to_be_clickable((By.XPATH, '//*[@id="pageContent"]/div[1]/div[3]/div[4]/div/div/button[2]')))submit.click()self.wait.until(EC.text_to_be_present_in_element((By.XPATH, '//*[@id="sortBarWrap"]/div[1]/div[2]/div[2]/div[8]/div/span/em'), str(page_number)))self.get_goods(page_number)except TimeoutException:print("page_number: error")self.page_turning(page_number)# 获取商品信息def get_goods(self, page):time.sleep(10)html = self.driver.page_sourcedoc = pq(html)items = doc('div.PageContent--contentWrap--mep7AEm > div.LeftLay--leftWrap--xBQipVc > div.LeftLay--leftContent--AMmPNfB > div.Content--content--sgSCZ12 > div > a').items()for item in items:title = item.find('.Title--title--jCOPvpf span').text()price_int = item.find('.Price--priceInt--ZlsSi_M').text()price_float = item.find('.Price--priceFloat--h2RR0RK').text()price = float(f"{price_int}{price_float}") if price_int and price_float else 0.0deal = item.find('.Price--realSales--FhTZc7U').text()location = item.find('.Price--procity--_7Vt3mX').text()shop = item.find('.ShopInfo--TextAndPic--yH0AZfx a').text()postText = "包邮" if "包邮" in item.find('.SalesPoint--subIconWrapper--s6vanNY span').text() else "/"t_url = item.attr('href')shop_url = item.find('.ShopInfo--TextAndPic--yH0AZfx a').attr('href')img_url = item.find('.MainPic--mainPicWrapper--iv9Yv90 img').attr('src')product = {'Page': page,'Num': self.count - 1,'title': title,'price': price,'deal': deal,'location': location,'shop': shop,'isPostFree': postText,'url': t_url,'shop_url': shop_url,'img_url': img_url}print(product)self.products.append(product)self.ws.cell(row=self.count, column=1, value=page)self.ws.cell(row=self.count, column=2, value=self.count - 1)self.ws.cell(row=self.count, column=3, value=title)self.ws.cell(row=self.count, column=4, value=price)self.ws.cell(row=self.count, column=5, value=deal)self.ws.cell(row=self.count, column=6, value=location)self.ws.cell(row=self.count, column=7, value=shop)self.ws.cell(row=self.count, column=8, value=postText)self.ws.cell(row=self.count, column=9, value=t_url)self.ws.cell(row=self.count, column=10, value=shop_url)self.ws.cell(row=self.count, column=11, value=img_url)self.count += 1self.download_image(img_url, f'img_{self.count - 1}')# 保存数据def save_excel(self, filename):self.wb.save(filename=filename)print(f"{filename} 存储成功~")# 保存jsondef save_json(self, filename):with open(filename, 'w', encoding='utf-8') as f:json.dump(self.products, f, ensure_ascii=False, indent=4)print(f"{filename} 存储成功~")# 运行def run(self):self.search_goods()excel_filename = 'data/服装(From-Tao).xlsx'self.save_excel(excel_filename)json_filename = 'data/content.json'self.save_json(json_filename)self.driver.quit()# 主函数
if __name__ == '__main__':crawler = TaobaoCrawler(keyword='服装', start_page=1, end_page=45)crawler.run()

代码分析

import json
import os
import requests
from selenium import webdriver
from selenium.common.exceptions import TimeoutException
from selenium.webdriver.common.by import By
from selenium.webdriver.support.ui import WebDriverWait
from selenium.webdriver.support import expected_conditions as EC
from pyquery import PyQuery as pq # 作用：解析网页
import time
import openpyxl as op

导入模块

模块	作用
json	用于处理 JSON 数据。
os	用于操作系统相关功能，如文件路径操作。
requests	用于发送 HTTP 请求。
selenium 相关模块	用于自动化浏览器操作。
pyquery	用于解析 HTML 文档。
time	用于时间相关操作。
openpyxl	用于操作 Excel 文件`

# 主函数
if __name__ == '__main__':crawler = TaobaoCrawler(keyword='服装', start_page=1, end_page=45)crawler.run()

代码首先从主函数进入，传入三个参数，运行TaobaoCrawler爬虫类

# 初始化def __init__(self, keyword, start_page, end_page):self.keyword = keywordself.start_page = start_pageself.end_page = end_pageself.count = 1self.driver = self.init_driver()self.wait = WebDriverWait(self.driver, 10)self.wb, self.ws = self.init_excel()self.products = []self.create_images_folder()# 创建图片文件夹def create_images_folder(self):if not os.path.exists('images'):os.makedirs('images')# 下载图片def download_image(self, img_url, img_name):if img_url:response = requests.get(img_url)if response.status_code == 200:with open(f'images/{img_name}.png', 'wb') as f:f.write(response.content)else:print(f"Image URL is None for {img_name}, skipping download.")# 初始化浏览器def init_driver(self):options = webdriver.ChromeOptions()options.add_experimental_option("excludeSwitches", ['enable-automation'])driver = webdriver.Chrome(options=options)driver.maximize_window()return driver# 初始化Exceldef init_excel(self):wb = op.Workbook()ws = wb.create_sheet(index=0)title_list = ['Page', 'Num', 'Title', 'Price', 'Deal', 'Location', 'Shop', 'IsPostFree', 'Title_URL', 'Shop_URL', 'Img_URL']for i, title in enumerate(title_list):ws.cell(row=self.count, column=i + 1, value=title)self.count += 1return wb, ws

初始化方法 _init：

初始化爬虫的关键字、起始页和结束页。
初始化浏览器驱动、等待对象、Excel 文件和工作表。
创建存储图片的文件夹。

方法 create_images_folder：检查并创建存储图片的文件夹。
方法 download_image：下载并保存图片到本地。
方法 init_driver：初始化并配置 Chrome 浏览器驱动。
方法 init_excel：初始化 Excel 文件和工作表，并设置表头。

# 搜索商品def search_goods(self):try:# 搜索商品print('正在搜索: ')self.driver.get('https://www.taobao.com')self.driver.execute_cdp_cmd("Page.addScriptToEvaluateOnNewDocument",{"source": """Object.defineProperty(navigator, 'webdriver', {get: () => undefined})"""})input = self.wait.until(EC.presence_of_element_located((By.CSS_SELECTOR, "#q")))# 提交搜索submit = self.wait.until(EC.element_to_be_clickable((By.CSS_SELECTOR, '#J_TSearchForm > div.search-button > button')))# 输入关键字input.send_keys(self.keyword)submit.click()# 等待页面加载完成if self.start_page != 1:time.sleep(3)page_input = self.wait.until(EC.presence_of_element_located((By.XPATH, '//*[@id="pageContent"]/div[1]/div[3]/div[4]/div/div/span[3]/input')))page_input.send_keys(self.start_page)admit = self.wait.until(EC.element_to_be_clickable((By.XPATH, '//*[@id="pageContent"]/div[1]/div[3]/div[4]/div/div/button[3]')))admit.click()# 获取商品信息self.get_goods(self.start_page)for i in range(self.start_page + 1, self.end_page + 1):self.page_turning(i)# 捕获异常except TimeoutException:print("search_goods: error")self.search_goods()

方法 search_goods：

打开淘宝主页，输入关键字并搜索商品。
如果起始页不是第一页，跳转到指定页码。
获取商品信息。

# 翻页def page_turning(self, page_number):print('正在翻页: ', page_number)try:time.sleep(2)submit = self.wait.until(EC.element_to_be_clickable((By.XPATH, '//*[@id="pageContent"]/div[1]/div[3]/div[4]/div/div/button[2]')))submit.click()self.wait.until(EC.text_to_be_present_in_element((By.XPATH, '//*[@id="sortBarWrap"]/div[1]/div[2]/div[2]/div[8]/div/span/em'), str(page_number)))self.get_goods(page_number)except TimeoutException:print("page_number: error")self.page_turning(page_number)

方法 page_turning：翻页并获取商品信息

# 获取商品信息def get_goods(self, page):time.sleep(10)html = self.driver.page_sourcedoc = pq(html)items = doc('div.PageContent--contentWrap--mep7AEm > div.LeftLay--leftWrap--xBQipVc > div.LeftLay--leftContent--AMmPNfB > div.Content--content--sgSCZ12 > div > a').items()for item in items:title = item.find('.Title--title--jCOPvpf span').text()price_int = item.find('.Price--priceInt--ZlsSi_M').text()price_float = item.find('.Price--priceFloat--h2RR0RK').text()price = float(f"{price_int}{price_float}") if price_int and price_float else 0.0deal = item.find('.Price--realSales--FhTZc7U').text()location = item.find('.Price--procity--_7Vt3mX').text()shop = item.find('.ShopInfo--TextAndPic--yH0AZfx a').text()postText = "包邮" if "包邮" in item.find('.SalesPoint--subIconWrapper--s6vanNY span').text() else "/"t_url = item.attr('href')shop_url = item.find('.ShopInfo--TextAndPic--yH0AZfx a').attr('href')img_url = item.find('.MainPic--mainPicWrapper--iv9Yv90 img').attr('src')product = {'Page': page,'Num': self.count - 1,'title': title,'price': price,'deal': deal,'location': location,'shop': shop,'isPostFree': postText,'url': t_url,'shop_url': shop_url,'img_url': img_url}print(product)self.products.append(product)self.ws.cell(row=self.count, column=1, value=page)self.ws.cell(row=self.count, column=2, value=self.count - 1)self.ws.cell(row=self.count, column=3, value=title)self.ws.cell(row=self.count, column=4, value=price)self.ws.cell(row=self.count, column=5, value=deal)self.ws.cell(row=self.count, column=6, value=location)self.ws.cell(row=self.count, column=7, value=shop)self.ws.cell(row=self.count, column=8, value=postText)self.ws.cell(row=self.count, column=9, value=t_url)self.ws.cell(row=self.count, column=10, value=shop_url)self.ws.cell(row=self.count, column=11, value=img_url)self.count += 1self.download_image(img_url, f'img_{self.count - 1}')

方法 get_goods：解析当前页面的商品信息并保存到 Excel 和 JSON 文件中。

# 保存数据def save_excel(self, filename):self.wb.save(filename=filename)print(f"{filename} 存储成功~")# 保存jsondef save_json(self, filename):with open(filename, 'w', encoding='utf-8') as f:json.dump(self.products, f, ensure_ascii=False, indent=4)print(f"{filename} 存储成功~")# 运行def run(self):self.search_goods()excel_filename = 'data/服装(From-Tao).xlsx'self.save_excel(excel_filename)json_filename = 'data/content.json'self.save_json(json_filename)self.driver.quit()

方法 save_excel：保存 Excel 文件。
方法 save_json：保存 JSON 文件。
方法 run：执行爬虫任务，保存数据并关闭浏览器。

三. 数据处理

const fs = require('fs').promises;
const path = require('path');let data = null;
let d = null;// 定义一个异步函数来加载数据
async function loadData() {try {const filePath = path.join(__dirname, '../data', 'content.json');const fileContent = await fs.readFile(filePath, 'utf8');data = JSON.parse(fileContent);console.log('Data loaded successfully:', data);} catch (error) {console.error('Failed to load data:', error);}
}// 在程序启动时调用loadData函数
loadData().then(() => {// if (data) {//     console.log('Global data:', data[0].title);// }console.log("data.length", data.length)const dict = data.reduce((acc, item) => {acc[item.title] = itemreturn acc}, {})// console.log("dict", dict)const dict_arr = []for (const title in dict) {dict_arr.push(dict[title])}
// console.log("arr", arr)console.log("dict_arr.length", dict_arr.length)d = dict_arr.reduce((acc, item) => {let flag = falsefor (const v of acc) {if (v.name === item.location) {v.value += 1flag = true}}if (flag === false) {acc.push({name: item.location,value: 1})}return acc}, [])// 按value值降序排序d.sort((a, b) => b.value - a.value);console.log("data_handle\n", d)}).catch(error => {// 处理加载数据时的错误console.error('Failed to load data during startup:', error);
});

导入模块：
fs：用于文件系统操作，使用 promises 以便使用异步函数。
path：用于处理和转换文件路径。
变量声明：
data：用于存储从 JSON 文件加载的数据。
d：用于存储处理后的数据。
函数 loadData：
1. 异步函数，用于加载 JSON 文件中的数据。
2. 使用 fs.readFile 读取文件内容，并将其解析为 JSON 对象。
  1. 如果读取成功，数据将存储在 data 变量中，并在控制台输出成功消息。
  2. 如果读取失败，捕获错误并在控制台输出错误消息。
数据处理：
1. 在程序启动时调用 loadData 函数，并在数据加载完成后进行处理。
2. 使用 reduce 方法将数据转换为字典 dict，其中键为商品标题，值为商品对象。
3. 将字典转换为数组 dict_arr，以便进一步处理。
4. 使用 reduce 方法将 dict_arr 转换为按 location 分组的数组 d，并统计每个 location 的商品数量。
5. 按 value 值对 d 进行降序排序。
6. 在控制台输出处理后的数据。

四. 数据可视化

<!DOCTYPE html>
<html lang="en" style="height: 100%">
<head><meta charset="utf-8"><title>Region Distribution Map</title><style>body, html {height: 100%;margin: 0;font-family: Arial, sans-serif;background-color: #f4f4f4;}#container {height: 100%;width: 100%;max-width: 1200px;margin: 0 auto;padding: 20px;box-shadow: 0 0 10px rgba(0, 0, 0, 0.1);background-color: #fff;border-radius: 8px;}</style>
</head>
<body>
<div id="container"></div><script src="./js/echarts.min.js"></script>
<script src="./js/china.js"></script>
<script>document.addEventListener('DOMContentLoaded', () => {const dom = document.getElementById('container');const myChart = echarts.init(dom, null, {renderer: 'canvas',useDirtyRect: false});const data = [{name: '福建', value: 716},{name: '浙江', value: 557},{name: '广东', value: 340},{name: '湖北', value: 206},{name: '江苏', value: 67},{name: '上海', value: 12},{name: '安徽', value: 10},{name: '北京', value: 7},{name: '山东', value: 4},{name: '江西', value: 3},{name: '湖南', value: 3},{name: '广西', value: 3},{name: '河南', value: 3},{name: '河北', value: 1},{name: '四川', value: 1},{name: '新疆', value: 1},{name: '辽宁', value: 1}];const option = {title: {text: '中国服装发出地',left: 'center',textStyle: {color: '#333',fontSize: 24}},tooltip: {trigger: 'item',formatter: '{b}: {c}'},visualMap: {min: 0,max: 700,left: 'left',top: 'bottom',text: ['High', 'Low'],calculable: true,inRange: {color: ['#e0ffff', '#006edd']}},series: [{name: '服装发出地',type: 'map',map: 'china',roam: true,label: {show: true},data: data}]};myChart.setOption(option);window.addEventListener('resize', myChart.resize);});
</script>
</body>
</html>

效果展示:
20240907153000

中间需要俩个js文件:

china.js

(function (root, factory) {if (typeof define === 'function' && define.amd) {// AMD. Register as an anonymous module.define(['exports', 'echarts'], factory);} else if (typeof exports === 'object' && typeof exports.nodeName !== 'string') {// CommonJSfactory(exports, require('echarts'));} else {// Browser globalsfactory({}, root.echarts);}
}(this, function (exports, echarts) {var log = function (msg) {if (typeof console !== 'undefined') {console && console.error && console.error(msg);}}if (!echarts) {log('ECharts is not Loaded');return;}if (!echarts.registerMap) {log('ECharts Map is not loaded')return;}echarts.registerMap('china', {"type":"FeatureCollection","features":[{"id":"710000","type":"Feature","geometry":{"type":"MultiPolygon","coordinates":[["@@°Ü¯Û"],["@@ƛĴÕƊÉɼģºðʀ\\ƎsÆNŌÔĚäœnÜƤɊĂǀĆĴžĤǊŨxĚĮǂƺòƌ‚–âÔ®ĮXŦţƸZûÐ‹ƕƑGđ¨ĭMó·ęcëƝɉlÝƯֹÅŃ^Ó·śŃǋƏďíåɛGɉ™¿@ăƑŽ¥ĘWǬÏĶŁâ"],["@@\\p|WoYG¿¥I†j@¢"],["@@
¡‰@ˆV^RqˆBbAŒnTXeRz¤Lž«³I"],["@@ÆEE—„kWqë @œ"],["@@fced"]],"encodeOffsets":[[[122886,24033]],[[123335,22980]],[[122375,24193]],[[122518,24117]],[[124427,22618]],[[124862,26043]]]},"properties":{"cp":[121.509062,25.044332],"name":"台湾","childNum":6}},{"id":"130000","type":"Feature","geometry":{"type":"MultiPolygon","coordinates":[["@@o~†Z]‚ªr‰ºc_ħ²G¼s`jÎŸnüsÂœłNX_“M`Ç½ÓnUK
Ĝēs¤©yrý§uģŒc†JŠ›e"],["@@U`Ts¿mÂ‚"],["@@oºƋÄd–eVŽDJj£€J|ÅdzÂ•Ft~žKŨ¸IÆv|”‡¢r}è†Žonb˜}`RÎÄn°ÒdÞ²„^®’lnÐèĄlðÓœ×]ªÆ}LiĂ±Ö`^°Ç¶p®đDcœŋ`–ZÔ’¶êqvFÆš†N®ĆTH®¦O’¾ŠIbÐã´BĐɢŴÆíȦp–ĐÞXR€·nndOž¤’OÀĈƒQg˜µFo|gȒęSWb©osx|hYh•gŃfmÖĩnº€TÌ’Sp›¢dYĤ¶UĈjl’ǐpäìë|³kÛfw²Xjz~ÂqbTŠÑ„ěŨ@|oM‡’zv¢ZrÃVw¬ŧĖ¸fŒ°ÐT€ªqŽs{Sž¯r æÝlNd®²Ğ ǆiGĘ‚J™¼lr}~K¨ŸƐÌWö€™ÆŠzRš¤lêmĞLÎ„’@¡|q]SvK€ÑcwpÏÏ†ĿćènĪWlĄkT}ˆJ”¤~ƒÈT„d„™pddʾĬŠ”ŽBVt„EÀ¢ôPĎƗè@~‚k–ü\\rÊĔÖæW_§¼F˜†´©òDòj’ˆYÈrbĞāøŀG{ƀ|¦ðrb|ÀH`pʞkv‚GpuARhÞÆǶgĘŠTǼƹS£¨¡ù³ŘÍ]¿Ây™ôEP xX¶¹Ü‡O¡“gÚ¡IwÃé‘¦ÅB‡Ï|Ç°
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<head><meta charset="utf-8"><title>Clothing Category Distribution</title><style>body {font-family: Arial, sans-serif;margin: 0;padding: 0;display: flex;justify-content: center;align-items: center;height: 100%;background-color: #f4f4f4;}#container {width: 80%;height: 80%;}</style>
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<body><div id="container"></div><script type="text/javascript" src="https://registry.npmmirror.com/echarts/5.5.1/files/dist/echarts.min.js"></script><script type="text/javascript">var dom = document.getElementById('container');var myChart = echarts.init(dom, null, {renderer: 'canvas',useDirtyRect: false});var app = {};var data = [{ name: '外套', value: 377 },{ name: '衬衫', value: 170 },{ name: '长袖', value: 200 },{ name: '卫衣', value: 57 },{ name: 'T恤', value: 108 },{ name: '内衣', value: 8 },{ name: '裤子', value: 734 },{ name: '裙子', value: 150 },{ name: '运动装', value: 805 }];// Sort data in descending orderdata.sort((a, b) => b.value - a.value);var option = {title: {text: 'Clothing Category Distribution',subtext: 'Based on the number of items',left: 'center',textStyle: {color: '#333',fontSize: 24},subtextStyle: {color: '#666',fontSize: 14}},tooltip: {trigger: 'axis',axisPointer: {type: 'shadow'},formatter: '{b}: {c} items'},grid: {left: '3%',right: '4%',bottom: '3%',containLabel: true},xAxis: {type: 'category',data: data.map(item => item.name),axisTick: {alignWithLabel: true},axisLabel: {interval: 0,rotate: 30,textStyle: {fontSize: 12}}},yAxis: {type: 'value',axisLabel: {textStyle: {fontSize: 12}},splitLine: {show: true,lineStyle: {color: '#ddd'}}},series: [{name: '数量',type: 'bar',barWidth: '60%',data: data.map(item => item.value),itemStyle: {color: new echarts.graphic.LinearGradient(0, 0, 0, 1, [{ offset: 0, color: '#83bff6' },{ offset: 0.5, color: '#188df0' },{ offset: 1, color: '#188df0' }])},showBackground: true,backgroundStyle: {color: 'rgba(180, 180, 180, 0.2)'},label: {show: true,position: 'top',formatter: '{c}'}}]};if (option && typeof option === 'object') {myChart.setOption(option);}window.addEventListener('resize', myChart.resize);</script>
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